Comparison of two types of classification in functional dyspepsia; postprandial distress or epigastric pain vs. a multidimensional cluster analysis using unsupervised learning

Abstract

Artificial intelligence methods using unsupervised learning tools can support problem solving by establishing unidentified grouping or classification patterns that allow typing subgroups for more individualized management. There are few studies that allow us to know the influence of digestive and extra-digestive symptoms in the classification of functional dyspepsia. This research carried out a cluster unsupervised learning analysis based on these symptoms to discriminate subtypes of dyspepsia and compare with one of the currently most accepted classifications. An exploratory cluster analysis was carried out in adults with functional dyspepsia according to digestive, extra-digestive and emotional symptoms. Grouping patterns were formed in such a way that within each group there was homogeneity in terms of the values adopted by each variable. The cluster analysis method was two-stage and the results of the classification pattern were compared with one of the most accepted classifications of functional dyspepsia. Of 184 cases, 157 met the inclusion criteria. The cluster analysis excluded 34 unclassifiable cases. Patients with type 1 dyspepsia (cluster one) presented improvement after treatment in 100% of cases, only a minority presented depressive symptoms. Patients with type 2 dyspepsia (cluster two) presented a higher probability of failure to treatment with proton pump inhibitor, suffered more frequently from sleep disorders, anxiety, depression, fibromyalgia, physical limitations or chronic pain of a non-digestive nature. This classification of dyspepsia by cluster analysis establishes a more holistic vision of dyspepsia in which extradigestive characteristics, affective sy ptoms, presence or absence of sleep disorders and chronic pain allow discriminating behavior and response to first-line management.